1 to 25 of 132 Remote MLOps Jobs

Bedrock, etc.). Desired: Experience deploying models as APIs/microservices in cloud-native environments. Familiarity with prompt tuning, embedding generation, vector search, and knowledge retrieval frameworks. Understanding of MLOps best practices, version control, CI/CD pipelines, and containerization (Docker, Kubernetes). Prior experience working with GenAI tools and response-augmented generation workflows. Education: Bachelor's or Master's More ❯

learning), including model evaluation, explainability, and selection for business-critical use cases Strong hands-on experience with cloud infrastructure (AWS), containerization (Docker), and orchestration (Jenkins, Airflow) Proven capability in MLOps, including CI/CD pipelines, model monitoring, versioning, and automated retraining Experience deploying and serving models through APIs (e.g., Flask, FastAPI) in both real-time and batch-processing environments Excellent More ❯

problem Experience with experiment design and conducting A/B tests Proficiency in Python, with the ability to write production-grade code and a good understanding of data engineering & MLOps Solid understanding of machine learning concepts, familiarity working with common frameworks such as sci-kit-learn, TensorFlow, or PyTorch Collaborative and humble great teammate with an ability to work wimulti More ❯

environments (AWS, GCP, or Azure) Ability to work independently and communicate effectively in a remote team Bonus Points Experience with Hugging Face Transformers , LangChain , or RAG pipelines Knowledge of MLOps tools (e.g., MLflow, Weights & Biases, Docker, Kubernetes) Exposure to data engineering or DevOps practices Contributions to open-source AI projects or research publications What We Offer Fully remote working A More ❯

environments (AWS, GCP, or Azure) Ability to work independently and communicate effectively in a remote team Bonus Points Experience with Hugging Face Transformers , LangChain , or RAG pipelines Knowledge of MLOps tools (e.g. MLflow, Weights & Biases, Docker, Kubernetes) Exposure to data engineering or DevOps practices Contributions to open-source AI projects or research publications What We Offer Fully remote working A More ❯

real-world tradeoffs. Fluency in Python and commonly used ML libraries (e.g. pandas, scikit-learn; experience with PyTorch or TensorFlow is a plus). Experience with model lifecycle management (MLOps), including monitoring, retraining, and model versioning. Ability to work across data infrastructure, from SQL to large-scale distributed data tools (Spark, etc.). Strong written and verbal communication skills, especially More ❯

Mistral. Model performance and optimization: Fine-tuning and optimizing LLMs for quality, latency, sustainability, and cost. Programming and NLP tools: Advanced Python, frameworks like PyTorch, TensorFlow, Hugging Face, LangChain. MLOps and deployment: Docker, Kubernetes, Azure ML Studio, MLFlow. Cloud and AI infrastructure: Experience with Azure Cloud for scalable deployment. Databases and data platforms: SQL, NoSQL, Snowflake, Databricks. OUR VALUES: CURIOSITY More ❯

problem Experience with experiment design and conducting A/B tests Proficiency in Python, with the ability to write production-grade code and a good understanding of data engineering & MLOps Solid understanding of machine learning concepts, familiarity working with common frameworks such as sci-kit-learn, TensorFlow, or PyTorch Collaborative and humble team player with an ability to work with More ❯

and experience with data warehousing solutions (Snowflake, BigQuery, Redshift) Experience with cloud platforms (AWS, Azure, GCP) and their ML and AI services (SageMaker, Azure ML, Vertex AI) Knowledge of MLOps tools including Docker, MLflow, Kubeflow, or similar platforms Experience with version control (Git) and collaborative development practices Excellent analytical thinking and problem-solving abilities Strong communication skills with ability to More ❯

using frameworks such as Transformers, PyTorch, or TensorFlow Strong Python skills, with the ability to write clean, modular, production-grade code, and a solid understanding of data engineering and MLOps principles Ability to lead end-to-end ML projects, work independently in ambiguous problem spaces, and mentor junior team members Strong collaboration and communication skills, with experience aligning technical approaches More ❯

and manage their data with efficiency and ease. You will work closely with people from a wide variety of domains, as well as our Insights, Analytics Engineers, Data Scientists, MLOps, MarTech and other Data Engineering teams. You will help manage our growing data needs and support increasingly complex business problems by building and promoting self-service tools and data best More ❯

or similar 1 5 years experience in AI/ML roles or relevant hands-on projects Nice to Have (Bonus) : NLP, Computer Vision, or Reinforcement Learning experience Knowledge of MLOps tools (MLflow, Kubeflow, etc.) Familiarity with SQL or Big Data tools (e.g., Spark) ️ Please apply only if you meet the skill and experience criteria. This role is open and hiring More ❯

the ability to balance research innovation and commercial value delivery. Comfortable operating across cloud environments (GCP, Azure, AWS) and deploying AI/ML systems at scale. Strong knowledge of MLOps practices and infrastructure including CI/CD, model versioning, orchestration, and monitoring. A collaborative and inspiring leader who brings out the best in technical teams and communicates complex concepts to More ❯

Not Essential): Familiarity with deep learning frameworks (e.g. TensorFlow, PyTorch) Exposure to cloud platforms (AWS, GCP, or Azure) Experience with experimental design, research methods, or academic publishing Understanding of MLOps, version control (Git), or containerisation (e.g. Docker) Benefits: 💰 Competitive Salary & Bonus : £35,000 – £45,000 plus bonus 🏡 Hybrid Working : A flexible mix of office and remote work 📈 Career Growth : Structured More ❯

Not Essential): Familiarity with deep learning frameworks (e.g. TensorFlow, PyTorch) Exposure to cloud platforms (AWS, GCP, or Azure) Experience with experimental design, research methods, or academic publishing Understanding of MLOps, version control (Git), or containerisation (e.g. Docker) Benefits: 💰 Competitive Salary & Bonus : £35,000 – £45,000 plus bonus 🏡 Hybrid Working : A flexible mix of office and remote work 📈 Career Growth : Structured More ❯

and fine-tuning Large Language Models Proficient in vector databases (Pinecone, Weaviate, Milvus) and embedding technologies Expert in building RAG (Retrieval-Augmented Generation) systems at scale Strong experience with MLOps practices and model deployment pipelines Proficient in cloud AI services (AWS SageMaker/Bedrock) Deep understanding of distributed systems and microservices architecture Expert in data pipeline platforms (Apache Kafka, Airflow More ❯

and fine-tuning Large Language Models Proficient in vector databases (Pinecone, Weaviate, Milvus) and embedding technologies Expert in building RAG (Retrieval-Augmented Generation) systems at scale Strong experience with MLOps practices and model deployment pipelines Proficient in cloud AI services (AWS SageMaker/Bedrock) Deep understanding of distributed systems and microservices architecture Expert in data pipeline platforms (Apache Kafka, Airflow More ❯

with cloud platforms like Microsoft Azure or Google Cloud Platform (GCP); familiarity with Databricks is a plus. Comfort working in cloud-based or secure environments, with an interest in MLOps or model deployment. Awareness and practice of responsible AI, data ethics, and governance frameworks. Willingness to explore emerging technologies such as agentic AI, GitHub Copilot, Microsoft Copilot and Copilot Studio. More ❯

experience with ML libraries (e.g., TensorFlow, PyTorch, scikit-learn) Solid understanding of machine learning algorithms and statistical methods Experience working with cloud platforms (AWS, GCP, or Azure) Familiarity with MLOps tools and practices is a plus A degree in Computer Science, Engineering, Mathematics, or a related field What We Offer Salary: £50,000–£60,000 depending on experience Hybrid working More ❯

Machine Learning, Cloud, DevOps & Software Good theoretical understanding of machine learning, neural networks, and other computer vision topics Hands-on experience with Python and PyTorch Hands-on experience with MLOps frameworks and platforms (We use ClearML!) Experience with additional programming languages and frameworks is a plus Experience working with cloud platforms (GCP, AWS, Azure) Experience using Generative AI tools such More ❯

will support your career growth with us. Experience in programming in multiple languages, including Java, JavaScript and Python. Knowledge of and exposure to coaching frameworks. Hands-on experience with MLOps, data engineering, or deployment of AI/ML models (real time and batch) in a business environment. Experience leading teams on AI-driven projects, such as recommendation systems, automation, or More ❯

with cloud platforms like Microsoft Azure or Google Cloud Platform (GCP); familiarity with Databricks is a plus. Comfort working in cloud-based or secure environments, with an interest in MLOps or model deployment. Awareness and practice of responsible AI, data ethics, and governance frameworks. Willingness to explore emerging technologies such as agentic AI, GitHub Copilot, Microsoft Copilot and Copilot Studio. More ❯

inference modules, to complement model deployment. Contribute to shared libraries and standardised practices for model development and deployment. Assist in building and maintaining model pipelines and participate in the MLOps lifecycle. Required Skills, Experience & Values The successful candidate will possess the following skills, abilities and experience: Have at least 3 years of commercial experience working with Python; familiarity with C# More ❯

solutions for clients—owning the architecture and development of robust, scalable data platforms that feed into machine learning models and intelligent applications. You’ll work closely with Data Scientists, MLOps Engineers, and domain experts to ensure clean, accessible, and production-grade data pipelines are in place to support experimentation and deployment. 🛠️ Key Responsibilities Build and maintain high-performance data pipelines More ❯

solutions for clients—owning the architecture and development of robust, scalable data platforms that feed into machine learning models and intelligent applications. You’ll work closely with Data Scientists, MLOps Engineers, and domain experts to ensure clean, accessible, and production-grade data pipelines are in place to support experimentation and deployment. 🛠️ Key Responsibilities Build and maintain high-performance data pipelines More ❯